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The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the "diameter calculation region" and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems. [ABSTRACT FROM AUTHOR]

This paper presents an actuated spring-loaded inverted pendulum model with a vertically constrained suspended load mass to predict the vertical GRF and energetics of walking and running. Experiments were performed to validate the model prediction accuracy of vertical GRF. The average correlation coefficient was greater than 0.97 during walking and 0.98 during running. The model's predictions of energy cost reduction were compared with experimental data from the literature, and the difference between the experimental and predicted results was less than 7%. The predicted results of characteristic forces and energy cost under different suspension stiffness and damping conditions showed a tradeoff when selecting the suspension parameters of elastically suspended backpacks. [ABSTRACT FROM AUTHOR]

In recent decades, the rapid development of the global economy has led to a substantial increase in energy consumption, subsequently resulting in the emission of a significant quantity of toxic gases into the environment. So far, gas sensors based on polymer field-effect transistors (PFETs), a highly practical and cost-efficient strategy, have garnered considerable attention, primarily attributed to their inherent advantages of offering a plethora of material choices, robust flexibility, and cost-effectiveness. Notably, the development of functional organic semiconductors (OSCs), such as poly(3-hexylthiophene-2,5-diyl) (P3HT), has been the subject of extensive scholarly investigation in recent years due to its widespread availability and remarkable sensing characteristics. This paper provides an exhaustive overview encompassing the production, functionalization strategies, and practical applications of gas sensors incorporating P3HT as the OSC layer. The exceptional sensing attributes and wide-ranging utility of P3HT position it as a promising candidate for improving PFET-based gas sensors. [ABSTRACT FROM AUTHOR]

Small and medium sized enterprises (SMEs) play a vital role in the an country's economic growth. Several developed nations have adopted environment friendly practices in their enterprises recently and due to rise in CO2 emission and increase in temperature immediate environment friendly practices are needed to be implemented globally. SMEs in developing countries are still in struggle for green practices adoption. SMEs in developing country like Pakistan need to implement green practices for a number of reasons. In order to fill the existing research gap, this research study aims to explore the key drivers motivating SMEs (especially food industry) to adopt green practices, further our research study is aiming at identify the major barriers hinder green practices implementation. Based on the extensive review of existing literature and experts' opinions and by utilizing Delphi method, a total 13 key drivers that motivate green practices implementation and 25 major barriers hindering adoption of green practices were identified. By the experts' recommendations, all the key drivers and major barriers influencing green practices adoption were ranked. Findings of our study showed that among the key drivers "Financial assistance/support from government" was the highest ranked key driver motivating SMEs to adopt green/environment-friendly practices followed by "Organizational commitment", "Promotion of green practices" and "Proper training about green practices". Results of our study further revealed that "Limited financial resources" was the highest ranked barrier hindering SMEs green practices adoption among 25 barriers followed by "Lack of HR capabilities", "Loose organizational structure" and "Lack of resources", respectively. Based on our research study findings, we propose a number of green/environment-friendly practices that overcome the barriers currently SMEs sector is facing. Our this study findings are anticipated to benefit authorities and enterprises to focus on the implementation of green/environment-friendly practices and formulate the necessary solutions to overwhelmed barriers hindering SMEs growth and adoption of green practices. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a common and highly disabling disease that imposes a heavy burden on individuals and society. Although physical therapy is recommended as an important method to relieve OA symptoms, patients cannot continue treatment after returning home. Research on Internet telerehabilitation for knee osteoarthritis (KOA) can reduce pain and improve patient quality of life, and Internet of Things (IoT)-based telerehabilitation is a new form of delivering rehabilitation. This study will evaluate the effect of telerehabilitation via IoT, as a medium to deliver exercises, on pain and walking in patients with KOA. Methods: This study is a single-blind randomized controlled trial. We will recruit 42 middle-aged and elderly patients with KOA aged ≥ 50 years and randomly divided into power cycling group, neuromuscular exercise group, and control group, and intervention will last for 12 weeks. Outcome measures will be taken at baseline and 4 weeks, 8 weeks, and 12 weeks post-intervention. The pre- and posttreatment differences in knee pain and physical function between participants undergoing power cycling and neuromuscular training and those in the control group will be determined by each scale. The effectiveness will be assessed by the Western Ontario and McMaster Universities Osteoarthritis Index Score (WOMAC) and an 11-point numerical pain rating scale. Walking function and quality of life will be assessed by the timed up and go and walk test, 6-min walk test, and quality of life health status questionnaires. Discussion: The findings from this trial will establish the feasibility and effectiveness of IoT-based power cycling and neuromuscular training on elderly patients with KOA in the community. As a result, this trial may help provide experimental evidence for finding a better exercise method suitable for elderly patients with KOA in the community. Trail registration: Chinese Clinical Trials Registry ChiCTR2200058924. Prospectively registered on 6 May 2022. [ABSTRACT FROM AUTHOR]

Recommender systems are ubiquitous and play an important role in most service-based applications. Since rich semantics lie in interactions between user and item, various models based on Graph Neural Network (GNN) are widely adopted to model the interactions and achieve excellent recommendation performance. Nevertheless, these models mainly learn nodes in the path between user and item separately. The path is also selected randomly, which would bring in low-quality paths and lead to suboptimal performance. To address the above issues, this paper proposes a model named Multi-View Attention neural network with high-quality paths (HP-MA). The path between user and item is viewed as an entirety, and a neural network is designed to distill path semantic embedding. Meanwhile, a multi-view attention model is provided to learn the influences between embedding of the path and user/item. Besides, we provide a method to construct HP-MA between target user and item, which would consider the length and inner details of the path. Moreover, a generalized version of our model (HP-MA*) is proposed to consider several kinds of multi-hop paths, simultaneously. Lastly, extensive experiments are conducted on several public datasets, the results demonstrate that our model can achieve better performance than the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Background: Neuromuscular and quadriceps exercises have been shown to be effective approaches to relieve pain and to improve function for patients with knee osteoarthritis. In this study, we aim to provide an informative feasible model in which therapeutic exercise and education will be undertaken with physiotherapy supervision and instruction via video link. We also aim to explore the relationship between program-induced pain alleviation/functional improvements and reduction in irritability, which might be mediated through program-induced psychosocial benefits.Methods: In this proposed two-parallel group (neuromuscular exercise versus quadriceps exercise), single-blinded, randomised controlled trial, participants aged ≥50 years with osteoarthritic knee pain will undergo a 12-week intervention, comprising video-linked education, supervised exercises, and a 12-week follow-up. Seven measurements will be taken to collect longitudinal data. A generalised estimating equation will be used to establish the adjusted difference in effectiveness on pain, function, irritability, and psychosocial outcomes between participants undertaking neuromuscular exercises and those undertaking quadriceps exercises. The primary outcomes are overall average pain in the knee joint during walking, as assessed through the 11-point Numerical Pain Rating Scale, and the Western Ontario and McMaster Universities osteoarthritis index physical function subscale. Furthermore, pressure pain threshold and changes in self-report pain scores pre-, during, and post-exercise were also measured as an indication of irritability. In addition, both the 6-min walk test and a timed up & go test were used to assess walking function performance. Finally, patients' emotions (e.g., fear and catastrophising), self-trust, needs in terms of disease knowledge, mental resilience, social support and health-related quality of life were investigated. Two four-wave cross-lagged models will be used to investigate directional relationships, aiming to investigate the complex mechanisms concerning the effects of exercise programmes.Discussion: Through summarising the study's strengths and limitations, this study may provide promising insights in terms of exercise therapy optimisation for people with knee osteoarthritis and/or other chronic pain within a psychosocial framework.Trial Registration: ChiCTR2100041978 (chictr.org.cn), January 10, 2021. [ABSTRACT FROM AUTHOR]

The complex signal represented by power load is affected by many factors, so the signal components are very complicated. So that, it is difficult to obtain satisfactory prediction accuracy by using a single model for the complex signal. In this case, wavelet decomposition is used to decompose the power load into a series of sub signals. The low frequency sub signal is remarkably periodic, and the high frequency sub signals can prove to be chaotic signals. Then the signals of different characteristics are predicted by different models. For the low frequency sub signal, the support vector machine (SVM) is adopted. In SVM model, air temperature and week attributes are included in model inputs. Especially the week attribute is represented by a 3-bit binary encoding, which represents Monday to Sunday. For the chaotic high frequency sub signals, the chaotic local prediction (CLP) model is adopted. In CLP model, the embedding dimension and time delay are key parameters, which determines the prediction accuracy. In order to find the optimal parameters, a segmentation validation algorithm is proposed in this paper. The algorithm segments the known power load according to the time sequence. Then, based on the segmentation data, the optimal parameters are chosen based on the prediction accuracy. Compared with a single model, the prediction accuracy of the proposed algorithm is improved obviously, which proves the effectiveness. [ABSTRACT FROM AUTHOR]